The field of ensemble learning is witnessing a significant shift towards deeper and more complex architectures. Researchers are exploring innovative methods to integrate multiple base learners and improve predictive performance. One key direction is the development of recursive ensemble frameworks that can handle high levels of complexity and feature redundancy. These frameworks are designed to prune weaker learners and mitigate early performance saturation, enabling deeper stacking and improved accuracy. Another important area of research is the application of ensemble learning to imbalanced and non-stationary data streams, where dynamic ensemble frameworks are being developed to address the challenges of unequal class distributions and concept drift. Additionally, there is a growing interest in developing ensemble methods for resource-constrained environments, such as edge inference, where lightweight backup models and multi-level ensemble learning are being proposed to provide fault tolerance and deployment flexibility. Noteworthy papers in this area include:

• RocketStack, which introduces a level-aware recursive ensemble framework that achieves deep recursive ensembling with tractable complexity.
• LSH-DynED, which proposes a dynamic ensemble framework with LSH-based undersampling for evolving multi-class imbalanced classification.
• MEL, which develops a multi-level ensemble learning framework for resource-constrained environments.
• Hellsemble, which presents a novel and interpretable ensemble framework that leverages dataset complexity during both training and inference.